This chapter introduces an agent-based modeling framework for reproducing micro behavior in economic experiments. It gives an overview of the theoretical concept which forms the foundation of the framework as well as short descriptions of two exemplary models based on experimental data. The heterogeneous agents are endowed with a number of attributes like cooperativeness and employ more or less complex heuristics during their decision-making processes. The attributes help to distinguish between agents, and the heuristics distinguish between behavioral classes. Through this design, agents can be modeled to behave like real humans and their decision making is observable and traceable, features that are important when agent-based models are to be used in collaborative planning or participatory model-building processes.
Key Terms in this Chapter
Public Goods and Common Pool Resources: Goods that have in common that it is difficult or impossible to exclude potential consumers from them. The difference between those two categories is the different degree of subtractability. The utility derived from public goods is not or only slightly diminished by others using the same good. Examples include coded law and fresh air. Common pool resources, on the other hand, are characterized by subtractability. Examples include the fish population in a lake and groundwater. There are goods that lie in between, for example infrastructure like highways: as long as its use is well below its capacity, one more car does not hinder the other cars; in rush hour, however, cars compete for space. While the main problem with Public Goods is the provision and corresponding free-rider behavior, the main issue with common pool resources is appropriation or over-appropriation. These problems are addressed by different experimental decision environments: voluntary contribution mechanism refers to the provision of public goods; appropriation experiments deal with (over-)appropriation of common pool resources (see Ostrom et al., 1994 ).
Heuristics: Simple decision-making processes that can be characterized as fast and frugal. In bounded rationality theory these heuristics are assumed to be adapted to certain decision environments. By exploiting the informational structure of the environment, heuristics can be both fast and accurate. An paradigmatic example is the recognition heuristic . It is applicable in decision environments in which the information and lack of information are structured according to a characteristic of the entities in question. If we are asked, for example, which English soccer team will win a match, and we have heard of one of the teams and not of the other, we tend to chose the one we know. And we tend to be correct with this choice (see Todd & Gigerenzer, 1999 AU7: The in-text citation "Todd & Gigerenzer, 1999" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Agent-Based Modeling: Modeling refers to the process of designing a software representation of a real-world system or a small part of it with the purpose of replicating or simulating specific features of the modeled system. In an agent-based model, the model behavior results from behavior of many small software entities called agents. This technique is used to model real-world systems comprised of many decision-making entities with inhomogeneous preferences, knowledge, and decision-making processes. An advantage of this method is that no assumptions need to be made about an aggregate or mean behavior. Instead, this aggregation is made by the model. See Davidsson (2002) for a topology of different modeling techniques including what he calls agent-based social simulation, and see Tesfatsion (2002) for a discussion of agent-based computational economics. Hare and Deadman (2004) discuss different uses of agent-based models in environmental management.
Aspiration Adaptation Theory: Part of bounded rationality theory, the idea is that humans have an aspiration level. If a choice promises to satisfy this aspiration level, it is made without an extensive search for an optimal strategy. Selten coined the term satisficing instead of optimizing. If, however, after some search, no satisficing alternative is found, the aspiration level can be adapted downwards. Then a choice can be made among the alternatives already found that satisfy the new aspiration level (see Selten, 1998 , 2001 ).
Experimental Economics: In Experimental Economics, behavior of human subjects is researched in controlled experiments with monetary incentives. The settings include simple games in which the subjects play with or against each other. Their decisions directly influence the payoffs they receive. By using stylized games in controlled situations, economic experiments produce comparable and reproducible data. Varying specific aspects in these experiments can help to understand which aspects of a decision situation influence human behavior in what way (see Kagel & Roth, 1995 AU6: The in-text citation "Kagel & Roth, 1995" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Bounded Rationality: A decision theory that rests on the assumptions that human cognitive capabilities are limited and that these limitations are adaptive with respect to the decision environments humans frequently encounter. Decision are thought to be made usually without elaborate calculations, but instead by using fast and frugal heuristics. These heuristics certainly have the advantage of speed and simplicity, but if they are well matched to a decision environment, they can even outperform maximizing calculations with respect to accuracy. The reason for this is that many decision environments are characterized by incomplete information and noise. The information we do have is usually structured in a specific way that clever heuristics can exploit (see Gigerenzer and Selten, 2001 ).